Isard Labs
Algorithmic trading research · Investor briefing
An Alpha Factory for systematic markets.
Search like a lab.·Reject like a fund.
Confidential — prepared for prospective investors · Not an offer to sell securities
The thesis
A frontier AI lab’s method, run with a quant fund’s discipline.
The engine
Search like a lab
The same compute-scaled, empirical method that lets AI labs predict the next token — pointed at the next price move. Hypotheses in, evidence out, at scale.
⇄
The discipline
Reject like a fund
The heritage that made systematic funds compound: reject almost everything. Only signal that is provably non-random earns capital.
The two meet at one gate — and we don’t sell subscriptions, we plug what survives into capital.
Why systematic
If you can’t prove yourself wrong, you can’t know if you’re right.
Discretionary trading runs on hardwired human biases — and can never answer the only question that matters: was it skill, or was it luck?
confirmation
loss aversion
overconfidence
recency
hindsight
Systematic is the only path that can be measured, repeated, and disproved. You can’t trade on intuition forever.
Why this is hard
The odds are brutal — and even a flawless backtest can be a lie.
97%
of committed day traders lose money over time — and they don’t improve with practice1
74–89%
of retail trading accounts lose money, per market regulators2
Killer 01
Overfitting
The strategy memorised the past. It cannot predict the future.
Killer 02
Look-ahead bias
Tomorrow’s information leaked into yesterday’s decision. Invisible. Fatal.
Killer 03
Selection bias
Test a hundred, report the best. That’s not an edge — it’s a lottery winner.
Test enough combinations and you are guaranteed to find a winner in pure noise.3 The only defence: search millions — then try to kill each one.
1 — Chague, De-Losso & Giovannetti, Day Trading for a Living? (2020). 2 — ESMA review of retail CFD accounts (2018). 3 — Bailey, Borwein, López de Prado & Zhu, The Probability of Backtest Overfitting (2014).The wedge
No AI agents. No black boxes. No vibes.
The market ships autonomous “AI agents” trading capital nobody should have trusted them with. Isard is the deliberate opposite.
- Deterministic. Same input, same output — rules-based, version-controlled, frozen before testing.
- Reproducible & auditable. Every result byte-for-byte; every spec signed; every change re-validated against the whole prior corpus.
- ML earns its place — never as the trader. It proposes; rules decide. If we can’t explain the trade, we don’t take it.
- Not high-frequency. We don’t race on microseconds, co-location, or latency. Our edge is research, not speed.
The gate
Before an idea touches money, it has to survive five checks.
01 · Prove it
Does it actually work?
We test the idea hard, across many different market conditions — not just the ones that flatter it.
02 · Attack it
Can we break it?
A separate team tries to tear it apart. If there’s a flaw, their job is to find it first.
03 · Write it down
Is it repeatable?
Every step is documented and locked. Nothing depends on one person remembering how it worked.
04 · Time it
Is now the moment?
Even a good idea has to meet the market at the right time, not just any time.
05 · Check reality
Did it behave?
Once it’s live, we compare every real trade to what we expected. Surprises get caught fast.
— · The IP
What we keep private
The exact methods stay ours. The discipline of running all five is the product.
What survives
We search wide and reject hard. Real capital is the final reviewer.
The space of possible strategies runs to the trillions. Five disciplines kill all but a few. What survives has earned the right to manage money.
Depth of research
Five years of research — an archive most funds never build.
5+ yrs
of research compounding into the architecture
8
complete research systems — each a different way to find edge (ML, reinforcement learning, market regimes, order flow…)
100K+
lines of research code, version-controlled
10K+
experiments — run, recorded, and reviewed
1,000s
of signal & indicator variants engineered and tested
1,000s
of method configurations compared head-to-head
Millions
of candidate combinations searched at scale
A few
survive the gate to reach real capital
Most of it was rejected — and that is the point. The archive of what didn’t work is the map of where the edge isn’t.
The moat — our origin
Two paths, 20+ years each — academia and the markets — converging into one lab.
Academic experience20+ years of study & research
University of São Paulo (USP)BSc Mathematics · 2009 · Brazil
Faculty of Technology of São Paulo (Fatec)BSc Systems Analysis & Dev. · 2014 · Brazil
University of LiverpoolMSc Big Data · 2019 · UK
University of AlbertaReinforcement Learning AI · 2020 · Canada
University of LiverpoolMSc Artificial Intelligence · 2021–23 · UK★ Dissertation of the Year
Isard Labs
THE SPIN-OFF
Isard LabsUniversidad LoyolaPhD Artificial Intelligence · 2027* · Spain
Professional experience20+ years · risk & quantitative roles
HSBCInvestment bank · ~US$3T assets · UK & Brazil · 10+ yrs
SantanderInvestment bank · ~US$2T assets · Brazil
ItaúInvestment bank · ~US$0.5T assets · Brazil
Banco VotorantimInvestment bank · ~US$25B assets · Brazil
DevoteamApplied-AI consultancy · France · since 2018
Isard Labs is the spin-off — where research method met market experience. Hard to hire. Harder to replicate.
Who built this
The rare profile where deep research and front-line trading live in one person.
- 10+ years inside a top-five global investment bank — market-risk analyst → senior quantitative developer → Quantitative Analytics Manager, owning the firm-wide modelling platform quant teams used to build and ship models.
- Since 2018 — applied AI beyond finance — machine learning and quantitative methods deployed across other industries and research domains, broadening the toolkit that now powers the lab.
- Academic depth — dual bachelor’s (Maths; Computer Science), dual master’s (Big Data; AI — Dissertation of the Year), PhD in progress in Applied Mathematics & Quantitative Finance; peer-reviewed publications in maths & ML journals.
- At the state of the art — builds on the newest quantitative methods and modern compute, not legacy tooling — the same frontier discipline that AI labs run.
- Real capital, real consequences — production systematic strategies, reconciled bar by bar. 20+ years across Brazil, the UK and Europe · Mensa · fluent EN/ES/PT/FR.
Why now
The compute era rewards disciplined search — and punishes hype.
The Isard — the Pyrenean chamois — is sure-footed where others slip. As capital floods toward unauditable “AI agents,” the durable edge belongs to whoever can search at scale and refuse to fool themselves about what they find.
Search like a lab.·Reject like a fund.·The moment compute makes that decisive is now.
The universe
What we point the search at — today crypto, expanding across asset classes.
Today we cover the top-20 crypto assets by market cap. The raise widens the same disciplined search into the world’s most liquid futures and ETFs:
Crypto · today
Top-20 by market cap
BTC, ETH, SOL, XRP, and the rest of the large-cap set — where the research began.
Equity-index futures
The benchmark indices
ES (S&P 500), NQ (Nasdaq-100), RTY (Russell 2000) — the deepest, most-traded equity markets.
Commodities
Energy & metals
CL (crude oil), GC (gold) — classic trend and macro markets.
Rates
Government bonds
ZN (10-yr Treasury note), ZB (30-yr Treasury bond) — the core of the interest-rate complex.
FX
Major currencies
6E (euro / USD), 6J (Japanese yen / USD) — the most liquid currency futures.
ETFs
One-click exposure
SPY (S&P 500), QQQ (Nasdaq-100), IWM (Russell 2000), TLT (long Treasuries), GLD (gold), USO (oil) + sector ETFs.
The opportunity
Managing US$1B is a rounding error in markets this size.
US$127T
global equity markets1
US$145T
global bond markets2
US$7.5T/day
foreign-exchange turnover3
US$4.5T
global hedge-fund industry4
Against this, a US$1B fund is a fraction of a fraction. The ceiling on this business isn’t market capacity — it’s how much validated edge we can manufacture. Compute lifts that ceiling.
1 — SIFMA / Visual Capitalist, global equity market cap (2024). 2 — SIFMA, global bond market (2024). 3 — BIS Triennial Survey, daily FX turnover (Apr 2022). 4 — HFR, hedge-fund industry AUM (2024).The business model
We don’t run the fund. We license the blueprint — and earn on every dollar it manages.
Isard Labs delivers a validated, ready-to-build strategy methodology. A fund — new or existing — builds and operates it. Our service contract: 1% per year on AUM (the capital the fund manages) + 10% of profits. Revenue scales with each client fund:
| AUM per fund | 1 fund | 5 funds | 10 funds |
|---|---|---|---|
| US$100M | US$3M | US$15M | US$30M |
| US$500M | US$15M | US$75M | US$150M |
| US$1B | US$30M | US$150M | US$300M |
Isard Labs annual revenue. We carry no market risk and no fund operations — we are paid to provide the edge.
Illustrative — 1%/yr AUM + 10% of profit, assuming each fund nets ~20%/yr (≈3% of AUM per fund). Fee terms indicative, pending contracts & legal review.The ask
US$50M over two years — to build the compute that scales the discipline.
US$50M
raise, deployed over ~24 months
~US$20M/yr
compute — generation (GPU/VRAM) + validation (CPU/RAM)
~US$5M/yr
team, compliance, legal
~US$1.7M/mo
compute to spend — most to generation, the rest to validation
a few ¢
hardware cost to test one strategy (~1 core · 10 min)
Millions/yr
strategies generated, tested, and run through the gate
The bottleneck between a validated 10% strategy and a validated 20–30% one is search at scale. This raise turns one machine into a fleet — and runs the candidates through the gate.
Valuation framing
Priced as an applied-AI research lab, not a traditional fund.
Floor
Cost to replicate the IP
5 years of research fused with two decades of academic + trading-floor experience — est. US$20–40M and years to rebuild.
Engine
Recurring licensing revenue
At ~US$1B of client AUM, our 1%/yr + 10%-profit fees (~US$30M/yr) cover the entire lab — every additional fund is upside, with no market risk to us.
Comps
Early AI labs
Priced pre-revenue, on team + thesis: Anthropic raised US$124M (Series A, 2021); OpenAI took US$1B from Microsoft (2019) at ~US$20B.1
Indicative structure: US$50M for 10–15% of the technology company → US$350–500M post-money.
1 — Anthropic, “Anthropic raises $124 million” (2021); TechCrunch, “Microsoft invests $1 billion in OpenAI” (2019). · Illustrative & non-binding, pending verification & legal review. Not an offer or solicitation.The close
Five disciplines. One standard.
Real capital is the final reviewer.
We’re raising US$50M to turn an award-winning, academically validated architecture into an institutional-scale Alpha Factory. If that’s a thesis you invest behind — let’s talk.
[ Contact: name · email · LinkedIn — to be inserted ]
Important notice
Confidential & not an offer.
This document is confidential and provided for discussion purposes only. It does not constitute investment advice, a recommendation, or an offer or solicitation to buy or sell any security or financial instrument, nor a prospectus or offering document. All figures — including strategy returns, AUM, breakeven, valuation, and projections — are illustrative, unaudited, and subject to verification, change, and legal review. Research-volume metrics describe development effort, not investment performance. Systematic and quantitative strategies carry risk, including loss of capital; past or simulated performance is not indicative of future results. Any offering of securities would be made only to qualified/eligible investors via definitive documentation and in compliance with applicable law. [ COMPLIANCE: final wording pending securities/legal review — do not distribute until approved. ]